mindspore.ops.scatter_mul

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mindspore.ops.scatter_mul(input_x, indices, updates)[source]

Perform a multiplication update on input_x based on the specified indices and update values.

\[\text{input_x}[\text{indices}[i, ..., j], :] \mathrel{*}= \text{updates}[i, ..., j, :]\]

Note

  • Support implicit type conversion and type promotion.

  • Since Parameter objects do not support type conversion, an exception will be thrown when input_x is of a low-precision data type.

  • The shape of updates is indices.shape + input_x.shape[1:].

Parameters

  • input_x (Union[Parameter, Tensor]) – The input parameter or tensor.

  • indices (Tensor) – The specified indices.

  • updates (Tensor) – The update values.

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> input_x = mindspore.Parameter(mindspore.tensor([[1.0, 1.0, 1.0],
...                               [2.0, 2.0, 2.0]], mindspore.float32), name="x")
>>> indices = mindspore.tensor([0, 1], mindspore.int32)
>>> updates = mindspore.tensor([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]], mindspore.float32)
>>> output = mindspore.ops.scatter_mul(input_x, indices, updates)
>>> print(output)
[[2. 2. 2.]
 [4. 4. 4.]]
>>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized.
>>> input_x = mindspore.Parameter(mindspore.tensor([[1.0, 1.0, 1.0],
...                                                [2.0, 2.0, 2.0]], mindspore.float32), name="x")
>>> # for indices = [[0, 1], [1, 1]]
>>> # step 1: [0, 1]
>>> # input_x[0] = [1.0, 1.0, 1.0] * [1.0, 1.0, 1.0] = [1.0, 1.0, 1.0]
>>> # input_x[1] = [2.0, 2.0, 2.0] * [3.0, 3.0, 3.0] = [6.0, 6.0, 6.0]
>>> # step 2: [1, 1]
>>> # input_x[1] = [6.0, 6.0, 6.0] * [7.0, 7.0, 7.0] = [42.0, 42.0, 42.0]
>>> # input_x[1] = [42.0, 42.0, 42.0] * [9.0, 9.0, 9.0] = [378.0, 378.0, 378.0]
>>> indices = mindspore.tensor([[0, 1], [1, 1]], mindspore.int32)
>>> updates = mindspore.tensor([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]],
...                            [[7.0, 7.0, 7.0], [9.0, 9.0, 9.0]]], mindspore.float32)
>>> output = mindspore.ops.scatter_mul(input_x, indices, updates)
>>> print(output)
[[  1.   1.   1.]
 [378. 378. 378.]]
>>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized.
>>> input_x = mindspore.Parameter(mindspore.tensor([[1.0, 1.0, 1.0],
...                                                [2.0, 2.0, 2.0]], mindspore.float32), name="x")
>>> # for indices = [[1, 0], [1, 1]]
>>> # step 1: [1, 0]
>>> # input_x[0] = [1.0, 1.0, 1.0] * [3.0, 3.0, 3.0] = [3.0, 3.0, 3.0]
>>> # input_x[1] = [2.0, 2.0, 2.0] * [1.0, 1.0, 1.0] = [2.0, 2.0, 2.0]
>>> # step 2: [1, 1]
>>> # input_x[1] = [2.0, 2.0, 2.0] * [7.0, 7.0, 7.0] = [14.0, 14.0, 14.0]
>>> # input_x[1] = [14.0, 14.0, 14.0] * [9.0, 9.0, 9.0] = [126.0, 126.0, 126.0]
>>> indices = mindspore.tensor([[1, 0], [1, 1]], mindspore.int32)
>>> updates = mindspore.tensor([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]],
...                            [[7.0, 7.0, 7.0], [9.0, 9.0, 9.0]]], mindspore.float32)
>>> output = mindspore.ops.scatter_mul(input_x, indices, updates)
>>> print(output)
[[  3.   3.   3.]
 [126. 126. 126.]]
>>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized.
>>> input_x = mindspore.Parameter(mindspore.tensor([[1.0, 1.0, 1.0],
...                                                [2.0, 2.0, 2.0]], mindspore.float32), name="x")
>>> # for indices = [[0, 1], [0, 1]]
>>> # step 1: [0, 1]
>>> # input_x[0] = [1.0, 1.0, 1.0] * [1.0, 1.0, 1.0] = [1.0, 1.0, 1.0]
>>> # input_x[1] = [2.0, 2.0, 2.0] * [3.0, 3.0, 3.0] = [6.0, 6.0, 6.0]
>>> # step 2: [0, 1]
>>> # input_x[0] = [1.0, 1.0, 1.0] * [7.0, 7.0, 7.0] = [7.0, 7.0, 7.0]
>>> # input_x[1] = [6.0, 6.0, 6.0] * [9.0, 9.0, 9.0] = [54.0, 54.0, 54.0]
>>> indices = mindspore.tensor([[0, 1], [0, 1]], mindspore.int32)
>>> updates = mindspore.tensor([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]],
...                            [[7.0, 7.0, 7.0], [9.0, 9.0, 9.0]]], mindspore.float32)
>>> output = mindspore.ops.scatter_mul(input_x, indices, updates)
>>> print(output)
[[ 7.  7.  7.]
 [54. 54. 54.]]